Overview

Dataset statistics

Number of variables27
Number of observations7535
Missing cells24808
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory216.0 B

Variable types

Categorical11
Numeric16

Warnings

INSTNM has a high cardinality: 7535 distinct values High cardinality
CITY has a high cardinality: 2514 distinct values High cardinality
STABBR has a high cardinality: 59 distinct values High cardinality
MD_EARN_WNE_P10 has a high cardinality: 598 distinct values High cardinality
GRAD_DEBT_MDN_SUPP has a high cardinality: 2038 distinct values High cardinality
SATVRMID is highly correlated with SATMTMIDHigh correlation
SATMTMID is highly correlated with SATVRMIDHigh correlation
HBCU has 371 (4.9%) missing values Missing
MENONLY has 371 (4.9%) missing values Missing
WOMENONLY has 371 (4.9%) missing values Missing
SATVRMID has 6350 (84.3%) missing values Missing
SATMTMID has 6339 (84.1%) missing values Missing
DISTANCEONLY has 371 (4.9%) missing values Missing
UGDS has 661 (8.8%) missing values Missing
UGDS_WHITE has 661 (8.8%) missing values Missing
UGDS_BLACK has 661 (8.8%) missing values Missing
UGDS_HISP has 661 (8.8%) missing values Missing
UGDS_ASIAN has 661 (8.8%) missing values Missing
UGDS_AIAN has 661 (8.8%) missing values Missing
UGDS_NHPI has 661 (8.8%) missing values Missing
UGDS_2MOR has 661 (8.8%) missing values Missing
UGDS_NRA has 661 (8.8%) missing values Missing
UGDS_UNKN has 661 (8.8%) missing values Missing
PPTUG_EF has 682 (9.1%) missing values Missing
PCTPELL has 686 (9.1%) missing values Missing
PCTFLOAN has 686 (9.1%) missing values Missing
UG25ABV has 817 (10.8%) missing values Missing
MD_EARN_WNE_P10 has 1122 (14.9%) missing values Missing
UGDS_NHPI is highly skewed (γ1 = 22.78419429) Skewed
INSTNM is uniformly distributed Uniform
INSTNM has unique values Unique
UGDS_WHITE has 242 (3.2%) zeros Zeros
UGDS_BLACK has 562 (7.5%) zeros Zeros
UGDS_HISP has 588 (7.8%) zeros Zeros
UGDS_ASIAN has 1561 (20.7%) zeros Zeros
UGDS_AIAN has 2442 (32.4%) zeros Zeros
UGDS_NHPI has 3520 (46.7%) zeros Zeros
UGDS_2MOR has 2036 (27.0%) zeros Zeros
UGDS_NRA has 3906 (51.8%) zeros Zeros
UGDS_UNKN has 2067 (27.4%) zeros Zeros
PPTUG_EF has 1903 (25.3%) zeros Zeros
PCTFLOAN has 687 (9.1%) zeros Zeros

Reproduction

Analysis started2021-04-20 19:39:12.550607
Analysis finished2021-04-20 19:39:46.171164
Duration33.62 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

INSTNM
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7535
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size59.0 KiB
Gemini School of Visual Arts & Communication
 
1
Hebrew Union College-Jewish Institute of Religion
 
1
Harrison College-Morrisville
 
1
Hypnosis Motivation Institute
 
1
Rasmussen College–Romeoville/Joliet
 
1
Other values (7530)
7530 

Length

Max length93
Median length30
Mean length30.53497014
Min length3

Characters and Unicode

Total characters230081
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7535 ?
Unique (%)100.0%

Sample

1st rowAlabama A & M University
2nd rowUniversity of Alabama at Birmingham
3rd rowAmridge University
4th rowUniversity of Alabama in Huntsville
5th rowAlabama State University
ValueCountFrequency (%)
Gemini School of Visual Arts & Communication1
 
< 0.1%
Hebrew Union College-Jewish Institute of Religion1
 
< 0.1%
Harrison College-Morrisville1
 
< 0.1%
Hypnosis Motivation Institute1
 
< 0.1%
Rasmussen College–Romeoville/Joliet1
 
< 0.1%
Columbia College-Hollywood1
 
< 0.1%
H Councill Trenholm State Community College1
 
< 0.1%
Pivot Point Academy-Evanston1
 
< 0.1%
Pellissippi State Community College1
 
< 0.1%
Summit College1
 
< 0.1%
Other values (7525)7525
99.9%
2021-04-20T13:39:46.722105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
college2267
 
8.0%
of1919
 
6.8%
university1195
 
4.2%
school673
 
2.4%
beauty572
 
2.0%
institute553
 
2.0%
community546
 
1.9%
technical522
 
1.8%
state381
 
1.3%
academy320
 
1.1%
Other values (5774)19340
68.4%

Most occurring characters

ValueCountFrequency (%)
e23636
 
10.3%
20766
 
9.0%
o16426
 
7.1%
i14711
 
6.4%
t14416
 
6.3%
l14342
 
6.2%
a14049
 
6.1%
n14009
 
6.1%
r10761
 
4.7%
s9419
 
4.1%
Other values (64)77546
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter176246
76.6%
Uppercase Letter29630
 
12.9%
Space Separator20767
 
9.0%
Dash Punctuation3039
 
1.3%
Other Punctuation365
 
0.2%
Decimal Number31
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%
Final Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e23636
13.4%
o16426
9.3%
i14711
 
8.3%
t14416
 
8.2%
l14342
 
8.1%
a14049
 
8.0%
n14009
 
7.9%
r10761
 
6.1%
s9419
 
5.3%
c5917
 
3.4%
Other values (17)38560
21.9%
ValueCountFrequency (%)
C6489
21.9%
S3138
10.6%
T2132
 
7.2%
U1894
 
6.4%
A1814
 
6.1%
B1773
 
6.0%
I1733
 
5.8%
M1678
 
5.7%
P1135
 
3.8%
H944
 
3.2%
Other values (16)6900
23.3%
ValueCountFrequency (%)
19
29.0%
29
29.0%
43
 
9.7%
32
 
6.5%
02
 
6.5%
92
 
6.5%
62
 
6.5%
51
 
3.2%
71
 
3.2%
ValueCountFrequency (%)
'171
46.8%
&156
42.7%
.20
 
5.5%
/13
 
3.6%
#5
 
1.4%
ValueCountFrequency (%)
20766
> 99.9%
 1
 
< 0.1%
ValueCountFrequency (%)
-3029
99.7%
10
 
0.3%
ValueCountFrequency (%)
(1
100.0%
ValueCountFrequency (%)
)1
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin205876
89.5%
Common24205
 
10.5%

Most frequent character per script

ValueCountFrequency (%)
e23636
 
11.5%
o16426
 
8.0%
i14711
 
7.1%
t14416
 
7.0%
l14342
 
7.0%
a14049
 
6.8%
n14009
 
6.8%
r10761
 
5.2%
s9419
 
4.6%
C6489
 
3.2%
Other values (43)67618
32.8%
ValueCountFrequency (%)
20766
85.8%
-3029
 
12.5%
'171
 
0.7%
&156
 
0.6%
.20
 
0.1%
/13
 
0.1%
10
 
< 0.1%
19
 
< 0.1%
29
 
< 0.1%
#5
 
< 0.1%
Other values (11)17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII230067
> 99.9%
Punctuation11
 
< 0.1%
None3
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e23636
 
10.3%
20766
 
9.0%
o16426
 
7.1%
i14711
 
6.4%
t14416
 
6.3%
l14342
 
6.2%
a14049
 
6.1%
n14009
 
6.1%
r10761
 
4.7%
s9419
 
4.1%
Other values (60)77532
33.7%
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
ValueCountFrequency (%)
í2
66.7%
 1
33.3%

CITY
Categorical

HIGH CARDINALITY

Distinct2514
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Memory size59.0 KiB
New York
 
87
Chicago
 
78
Houston
 
72
Los Angeles
 
56
Miami
 
51
Other values (2509)
7191 

Length

Max length24
Median length9
Mean length8.819110816
Min length3

Characters and Unicode

Total characters66452
Distinct characters58
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1418 ?
Unique (%)18.8%

Sample

1st rowNormal
2nd rowBirmingham
3rd rowMontgomery
4th rowHuntsville
5th rowMontgomery
ValueCountFrequency (%)
New York87
 
1.2%
Chicago78
 
1.0%
Houston72
 
1.0%
Los Angeles56
 
0.7%
Miami51
 
0.7%
San Antonio49
 
0.7%
Dallas48
 
0.6%
Philadelphia46
 
0.6%
Brooklyn46
 
0.6%
Jacksonville41
 
0.5%
Other values (2504)6961
92.4%
2021-04-20T13:39:47.127590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city190
 
2.0%
san177
 
1.8%
new151
 
1.6%
york101
 
1.1%
chicago80
 
0.8%
park79
 
0.8%
fort77
 
0.8%
saint76
 
0.8%
houston73
 
0.8%
beach71
 
0.7%
Other values (2332)8497
88.8%

Most occurring characters

ValueCountFrequency (%)
a6126
 
9.2%
e5810
 
8.7%
o5146
 
7.7%
n5037
 
7.6%
l4318
 
6.5%
i4218
 
6.3%
r4000
 
6.0%
t3494
 
5.3%
s2951
 
4.4%
2045
 
3.1%
Other values (48)23307
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54711
82.3%
Uppercase Letter9657
 
14.5%
Space Separator2045
 
3.1%
Other Punctuation25
 
< 0.1%
Dash Punctuation14
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a6126
11.2%
e5810
10.6%
o5146
9.4%
n5037
9.2%
l4318
 
7.9%
i4218
 
7.7%
r4000
 
7.3%
t3494
 
6.4%
s2951
 
5.4%
u1625
 
3.0%
Other values (18)11986
21.9%
ValueCountFrequency (%)
C1011
 
10.5%
S976
 
10.1%
B732
 
7.6%
M721
 
7.5%
P673
 
7.0%
L620
 
6.4%
A589
 
6.1%
H494
 
5.1%
W446
 
4.6%
R406
 
4.2%
Other values (16)2989
31.0%
ValueCountFrequency (%)
.20
80.0%
'5
 
20.0%
ValueCountFrequency (%)
2045
100.0%
ValueCountFrequency (%)
-14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64368
96.9%
Common2084
 
3.1%

Most frequent character per script

ValueCountFrequency (%)
a6126
 
9.5%
e5810
 
9.0%
o5146
 
8.0%
n5037
 
7.8%
l4318
 
6.7%
i4218
 
6.6%
r4000
 
6.2%
t3494
 
5.4%
s2951
 
4.6%
u1625
 
2.5%
Other values (44)21643
33.6%
ValueCountFrequency (%)
2045
98.1%
.20
 
1.0%
-14
 
0.7%
'5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII66449
> 99.9%
None3
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
a6126
 
9.2%
e5810
 
8.7%
o5146
 
7.7%
n5037
 
7.6%
l4318
 
6.5%
i4218
 
6.3%
r4000
 
6.0%
t3494
 
5.3%
s2951
 
4.4%
2045
 
3.1%
Other values (46)23304
35.1%
ValueCountFrequency (%)
ó2
66.7%
í1
33.3%

STABBR
Categorical

HIGH CARDINALITY

Distinct59
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size59.0 KiB
CA
773 
TX
 
472
NY
 
459
FL
 
436
PA
 
394
Other values (54)
5001 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters15070
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL
ValueCountFrequency (%)
CA773
 
10.3%
TX472
 
6.3%
NY459
 
6.1%
FL436
 
5.8%
PA394
 
5.2%
OH352
 
4.7%
IL300
 
4.0%
MI207
 
2.7%
NC204
 
2.7%
MA194
 
2.6%
Other values (49)3744
49.7%
2021-04-20T13:39:47.443580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca773
 
10.3%
tx472
 
6.3%
ny459
 
6.1%
fl436
 
5.8%
pa394
 
5.2%
oh352
 
4.7%
il300
 
4.0%
mi207
 
2.7%
nc204
 
2.7%
ma194
 
2.6%
Other values (49)3744
49.7%

Most occurring characters

ValueCountFrequency (%)
A2392
15.9%
N1541
 
10.2%
C1341
 
8.9%
M1039
 
6.9%
I964
 
6.4%
L953
 
6.3%
O909
 
6.0%
T892
 
5.9%
Y576
 
3.8%
P544
 
3.6%
Other values (14)3919
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15070
100.0%

Most frequent character per category

ValueCountFrequency (%)
A2392
15.9%
N1541
 
10.2%
C1341
 
8.9%
M1039
 
6.9%
I964
 
6.4%
L953
 
6.3%
O909
 
6.0%
T892
 
5.9%
Y576
 
3.8%
P544
 
3.6%
Other values (14)3919
26.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15070
100.0%

Most frequent character per script

ValueCountFrequency (%)
A2392
15.9%
N1541
 
10.2%
C1341
 
8.9%
M1039
 
6.9%
I964
 
6.4%
L953
 
6.3%
O909
 
6.0%
T892
 
5.9%
Y576
 
3.8%
P544
 
3.6%
Other values (14)3919
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15070
100.0%

Most frequent character per block

ValueCountFrequency (%)
A2392
15.9%
N1541
 
10.2%
C1341
 
8.9%
M1039
 
6.9%
I964
 
6.4%
L953
 
6.3%
O909
 
6.0%
T892
 
5.9%
Y576
 
3.8%
P544
 
3.6%
Other values (14)3919
26.0%

HBCU
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing371
Missing (%)4.9%
Memory size59.0 KiB
0.0
7062 
1.0
 
102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21492
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.07062
93.7%
1.0102
 
1.4%
(Missing)371
 
4.9%
2021-04-20T13:39:47.687045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:47.774720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07062
98.6%
1.0102
 
1.4%

Most occurring characters

ValueCountFrequency (%)
014226
66.2%
.7164
33.3%
1102
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14328
66.7%
Other Punctuation7164
33.3%

Most frequent character per category

ValueCountFrequency (%)
014226
99.3%
1102
 
0.7%
ValueCountFrequency (%)
.7164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21492
100.0%

Most frequent character per script

ValueCountFrequency (%)
014226
66.2%
.7164
33.3%
1102
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21492
100.0%

Most frequent character per block

ValueCountFrequency (%)
014226
66.2%
.7164
33.3%
1102
 
0.5%

MENONLY
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing371
Missing (%)4.9%
Memory size59.0 KiB
0.0
7098 
1.0
 
66

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21492
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07098
94.2%
1.066
 
0.9%
(Missing)371
 
4.9%
2021-04-20T13:39:48.025913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:48.114359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07098
99.1%
1.066
 
0.9%

Most occurring characters

ValueCountFrequency (%)
014262
66.4%
.7164
33.3%
166
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14328
66.7%
Other Punctuation7164
33.3%

Most frequent character per category

ValueCountFrequency (%)
014262
99.5%
166
 
0.5%
ValueCountFrequency (%)
.7164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21492
100.0%

Most frequent character per script

ValueCountFrequency (%)
014262
66.4%
.7164
33.3%
166
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII21492
100.0%

Most frequent character per block

ValueCountFrequency (%)
014262
66.4%
.7164
33.3%
166
 
0.3%

WOMENONLY
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing371
Missing (%)4.9%
Memory size59.0 KiB
0.0
7126 
1.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21492
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07126
94.6%
1.038
 
0.5%
(Missing)371
 
4.9%
2021-04-20T13:39:48.320508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:48.386101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07126
99.5%
1.038
 
0.5%

Most occurring characters

ValueCountFrequency (%)
014290
66.5%
.7164
33.3%
138
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14328
66.7%
Other Punctuation7164
33.3%

Most frequent character per category

ValueCountFrequency (%)
014290
99.7%
138
 
0.3%
ValueCountFrequency (%)
.7164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21492
100.0%

Most frequent character per script

ValueCountFrequency (%)
014290
66.5%
.7164
33.3%
138
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII21492
100.0%

Most frequent character per block

ValueCountFrequency (%)
014290
66.5%
.7164
33.3%
138
 
0.2%

RELAFFIL
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 KiB
0
6096 
1
1439 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7535
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%
2021-04-20T13:39:48.538936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:48.621539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%

Most occurring characters

ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7535
100.0%

Most frequent character per category

ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common7535
100.0%

Most frequent character per script

ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7535
100.0%

Most frequent character per block

ValueCountFrequency (%)
06096
80.9%
11439
 
19.1%

SATVRMID
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct163
Distinct (%)13.8%
Missing6350
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean522.8194093
Minimum290
Maximum765
Zeros0
Zeros (%)0.0%
Memory size59.0 KiB
2021-04-20T13:39:48.726343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile430
Q1475
median510
Q3555
95-th percentile665
Maximum765
Range475
Interquartile range (IQR)80

Descriptive statistics

Standard deviation68.57886165
Coefficient of variation (CV)0.1311712236
Kurtosis1.074681792
Mean522.8194093
Median Absolute Deviation (MAD)40
Skewness0.8192137191
Sum619541
Variance4703.060265
MonotocityNot monotonic
2021-04-20T13:39:48.848897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49552
 
0.7%
47543
 
0.6%
50039
 
0.5%
47037
 
0.5%
49036
 
0.5%
52034
 
0.5%
53034
 
0.5%
50534
 
0.5%
51032
 
0.4%
48031
 
0.4%
Other values (153)813
 
10.8%
(Missing)6350
84.3%
ValueCountFrequency (%)
2901
< 0.1%
3602
< 0.1%
3631
< 0.1%
3651
< 0.1%
3802
< 0.1%
ValueCountFrequency (%)
7651
< 0.1%
7601
< 0.1%
7551
< 0.1%
7501
< 0.1%
7452
< 0.1%

SATMTMID
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct167
Distinct (%)14.0%
Missing6339
Missing (%)84.1%
Infinite0
Infinite (%)0.0%
Mean530.7650502
Minimum310
Maximum785
Zeros0
Zeros (%)0.0%
Memory size59.0 KiB
2021-04-20T13:39:48.963555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile430
Q1482
median520
Q3565
95-th percentile685
Maximum785
Range475
Interquartile range (IQR)83

Descriptive statistics

Standard deviation73.4697671
Coefficient of variation (CV)0.1384223906
Kurtosis0.8642076997
Mean530.7650502
Median Absolute Deviation (MAD)40
Skewness0.8598872434
Sum634795
Variance5397.806677
MonotocityNot monotonic
2021-04-20T13:39:49.085933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49047
 
0.6%
52043
 
0.6%
50042
 
0.6%
48038
 
0.5%
48536
 
0.5%
51035
 
0.5%
50535
 
0.5%
52534
 
0.5%
49533
 
0.4%
51533
 
0.4%
Other values (157)820
 
10.9%
(Missing)6339
84.1%
ValueCountFrequency (%)
3101
< 0.1%
3601
< 0.1%
3651
< 0.1%
3681
< 0.1%
3752
< 0.1%
ValueCountFrequency (%)
7851
 
< 0.1%
7702
< 0.1%
7602
< 0.1%
7581
 
< 0.1%
7553
< 0.1%

DISTANCEONLY
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing371
Missing (%)4.9%
Memory size59.0 KiB
0.0
7124 
1.0
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21492
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.07124
94.5%
1.040
 
0.5%
(Missing)371
 
4.9%
2021-04-20T13:39:49.314684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:49.399094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07124
99.4%
1.040
 
0.6%

Most occurring characters

ValueCountFrequency (%)
014288
66.5%
.7164
33.3%
140
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14328
66.7%
Other Punctuation7164
33.3%

Most frequent character per category

ValueCountFrequency (%)
014288
99.7%
140
 
0.3%
ValueCountFrequency (%)
.7164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21492
100.0%

Most frequent character per script

ValueCountFrequency (%)
014288
66.5%
.7164
33.3%
140
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII21492
100.0%

Most frequent character per block

ValueCountFrequency (%)
014288
66.5%
.7164
33.3%
140
 
0.2%

UGDS
Real number (ℝ≥0)

MISSING

Distinct2932
Distinct (%)42.7%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean2356.83794
Minimum0
Maximum151558
Zeros7
Zeros (%)0.1%
Memory size59.0 KiB
2021-04-20T13:39:49.506271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.65
Q1117
median412.5
Q31929.5
95-th percentile11858.05
Maximum151558
Range151558
Interquartile range (IQR)1812.5

Descriptive statistics

Standard deviation5474.275871
Coefficient of variation (CV)2.322720531
Kurtosis103.7582136
Mean2356.83794
Median Absolute Deviation (MAD)361.5
Skewness6.806530158
Sum16200904
Variance29967696.31
MonotocityNot monotonic
2021-04-20T13:39:49.651378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3929
 
0.4%
6126
 
0.3%
4626
 
0.3%
5826
 
0.3%
3825
 
0.3%
6025
 
0.3%
4725
 
0.3%
9524
 
0.3%
4324
 
0.3%
6324
 
0.3%
Other values (2922)6620
87.9%
(Missing)661
 
8.8%
ValueCountFrequency (%)
07
0.1%
13
< 0.1%
23
< 0.1%
33
< 0.1%
44
0.1%
ValueCountFrequency (%)
1515581
< 0.1%
776571
< 0.1%
614701
< 0.1%
599201
< 0.1%
580841
< 0.1%

UGDS_WHITE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct4397
Distinct (%)64.0%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.510207201
Minimum0
Maximum1
Zeros242
Zeros (%)3.2%
Memory size59.0 KiB
2021-04-20T13:39:49.784014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013265
Q10.2675
median0.5557
Q30.747875
95-th percentile0.927315
Maximum1
Range1
Interquartile range (IQR)0.480375

Descriptive statistics

Standard deviation0.286958349
Coefficient of variation (CV)0.5624349254
Kurtosis-1.079241841
Mean0.510207201
Median Absolute Deviation (MAD)0.227
Skewness-0.2560443813
Sum3507.1643
Variance0.08234509408
MonotocityNot monotonic
2021-04-20T13:39:49.943534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0242
 
3.2%
1109
 
1.4%
0.666722
 
0.3%
0.518
 
0.2%
0.815
 
0.2%
0.613
 
0.2%
0.833313
 
0.2%
0.333313
 
0.2%
0.571412
 
0.2%
0.857111
 
0.1%
Other values (4387)6406
85.0%
(Missing)661
 
8.8%
ValueCountFrequency (%)
0242
3.2%
0.00042
 
< 0.1%
0.00052
 
< 0.1%
0.00071
 
< 0.1%
0.0011
 
< 0.1%
ValueCountFrequency (%)
1109
1.4%
0.99681
 
< 0.1%
0.99641
 
< 0.1%
0.9951
 
< 0.1%
0.99481
 
< 0.1%

UGDS_BLACK
Real number (ℝ≥0)

MISSING
ZEROS

Distinct3242
Distinct (%)47.2%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.1899966395
Minimum0
Maximum1
Zeros562
Zeros (%)7.5%
Memory size59.0 KiB
2021-04-20T13:39:50.100340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.036125
median0.10005
Q30.2577
95-th percentile0.726715
Maximum1
Range1
Interquartile range (IQR)0.221575

Descriptive statistics

Standard deviation0.224586518
Coefficient of variation (CV)1.182055212
Kurtosis2.485739193
Mean0.1899966395
Median Absolute Deviation (MAD)0.08145
Skewness1.733201177
Sum1306.0369
Variance0.05043910407
MonotocityNot monotonic
2021-04-20T13:39:50.274370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0562
 
7.5%
128
 
0.4%
0.047616
 
0.2%
0.142915
 
0.2%
0.034514
 
0.2%
0.111114
 
0.2%
0.187513
 
0.2%
0.166713
 
0.2%
0.213
 
0.2%
0.058813
 
0.2%
Other values (3232)6173
81.9%
(Missing)661
 
8.8%
ValueCountFrequency (%)
0562
7.5%
0.00021
 
< 0.1%
0.00031
 
< 0.1%
0.00051
 
< 0.1%
0.00061
 
< 0.1%
ValueCountFrequency (%)
128
0.4%
0.9941
 
< 0.1%
0.99111
 
< 0.1%
0.99081
 
< 0.1%
0.98991
 
< 0.1%

UGDS_HISP
Real number (ℝ≥0)

MISSING
ZEROS

Distinct2809
Distinct (%)40.9%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.1616348851
Minimum0
Maximum1
Zeros588
Zeros (%)7.8%
Memory size59.0 KiB
2021-04-20T13:39:50.445399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0276
median0.0714
Q30.198875
95-th percentile0.6667
Maximum1
Range1
Interquartile range (IQR)0.171275

Descriptive statistics

Standard deviation0.2218537907
Coefficient of variation (CV)1.372561317
Kurtosis4.88840692
Mean0.1616348851
Median Absolute Deviation (MAD)0.056
Skewness2.251145805
Sum1111.0782
Variance0.04921910443
MonotocityNot monotonic
2021-04-20T13:39:50.571760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0588
 
7.8%
1136
 
1.8%
0.043516
 
0.2%
0.111116
 
0.2%
0.055616
 
0.2%
0.115
 
0.2%
0.215
 
0.2%
0.035915
 
0.2%
0.041715
 
0.2%
0.095215
 
0.2%
Other values (2799)6027
80.0%
(Missing)661
 
8.8%
ValueCountFrequency (%)
0588
7.8%
0.00073
 
< 0.1%
0.0011
 
< 0.1%
0.00122
 
< 0.1%
0.00142
 
< 0.1%
ValueCountFrequency (%)
1136
1.8%
0.99911
 
< 0.1%
0.99811
 
< 0.1%
0.99781
 
< 0.1%
0.99743
 
< 0.1%

UGDS_ASIAN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1254
Distinct (%)18.2%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.03354423916
Minimum0
Maximum0.9727
Zeros1561
Zeros (%)20.7%
Memory size59.0 KiB
2021-04-20T13:39:50.694465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0025
median0.0129
Q30.0327
95-th percentile0.131775
Maximum0.9727
Range0.9727
Interquartile range (IQR)0.0302

Descriptive statistics

Standard deviation0.07377719782
Coefficient of variation (CV)2.199399947
Kurtosis58.81975651
Mean0.03354423916
Median Absolute Deviation (MAD)0.0129
Skewness6.489844249
Sum230.5831
Variance0.005443074919
MonotocityNot monotonic
2021-04-20T13:39:50.821128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01561
 
20.7%
0.007430
 
0.4%
0.008627
 
0.4%
0.011226
 
0.3%
0.00725
 
0.3%
0.00925
 
0.3%
0.006424
 
0.3%
0.015424
 
0.3%
0.005424
 
0.3%
0.009524
 
0.3%
Other values (1244)5084
67.5%
(Missing)661
 
8.8%
ValueCountFrequency (%)
01561
20.7%
0.00023
 
< 0.1%
0.00031
 
< 0.1%
0.00042
 
< 0.1%
0.00052
 
< 0.1%
ValueCountFrequency (%)
0.97271
< 0.1%
0.9671
< 0.1%
0.96581
< 0.1%
0.95951
< 0.1%
0.95241
< 0.1%

UGDS_AIAN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct601
Distinct (%)8.7%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.0138125691
Minimum0
Maximum1
Zeros2442
Zeros (%)32.4%
Memory size59.0 KiB
2021-04-20T13:39:50.953072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0026
Q30.0073
95-th percentile0.03641
Maximum1
Range1
Interquartile range (IQR)0.0073

Descriptive statistics

Standard deviation0.07019573509
Coefficient of variation (CV)5.082018745
Kurtosis134.7402217
Mean0.0138125691
Median Absolute Deviation (MAD)0.0026
Skewness11.08329272
Sum94.9476
Variance0.004927441224
MonotocityNot monotonic
2021-04-20T13:39:51.110828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02442
32.4%
0.00268
 
0.9%
0.002264
 
0.8%
0.002463
 
0.8%
0.001560
 
0.8%
0.002358
 
0.8%
0.003658
 
0.8%
0.001857
 
0.8%
0.004355
 
0.7%
0.002554
 
0.7%
Other values (591)3895
51.7%
(Missing)661
 
8.8%
ValueCountFrequency (%)
02442
32.4%
0.00035
 
0.1%
0.000421
 
0.3%
0.000522
 
0.3%
0.000620
 
0.3%
ValueCountFrequency (%)
12
< 0.1%
0.99441
< 0.1%
0.98941
< 0.1%
0.98871
< 0.1%
0.98211
< 0.1%

UGDS_NHPI
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct363
Distinct (%)5.3%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.004568897294
Minimum0
Maximum0.9983
Zeros3520
Zeros (%)46.7%
Memory size59.0 KiB
2021-04-20T13:39:51.253149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0025
95-th percentile0.0152
Maximum0.9983
Range0.9983
Interquartile range (IQR)0.0025

Descriptive statistics

Standard deviation0.0331250764
Coefficient of variation (CV)7.25012498
Kurtosis600.1361399
Mean0.004568897294
Median Absolute Deviation (MAD)0
Skewness22.78419429
Sum31.4066
Variance0.001097270687
MonotocityNot monotonic
2021-04-20T13:39:51.384066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03520
46.7%
0.0007105
 
1.4%
0.000998
 
1.3%
0.001195
 
1.3%
0.000893
 
1.2%
0.00193
 
1.2%
0.000692
 
1.2%
0.001485
 
1.1%
0.001883
 
1.1%
0.000478
 
1.0%
Other values (353)2532
33.6%
(Missing)661
 
8.8%
ValueCountFrequency (%)
03520
46.7%
0.00019
 
0.1%
0.000231
 
0.4%
0.000337
 
0.5%
0.000478
 
1.0%
ValueCountFrequency (%)
0.99831
< 0.1%
0.99171
< 0.1%
0.98811
< 0.1%
0.95381
< 0.1%
0.91931
< 0.1%

UGDS_2MOR
Real number (ℝ≥0)

MISSING
ZEROS

Distinct957
Distinct (%)13.9%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.0239503055
Minimum0
Maximum0.5333
Zeros2036
Zeros (%)27.0%
Memory size59.0 KiB
2021-04-20T13:39:51.498860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0175
Q30.0339
95-th percentile0.0769
Maximum0.5333
Range0.5333
Interquartile range (IQR)0.0339

Descriptive statistics

Standard deviation0.03128804781
Coefficient of variation (CV)1.306373641
Kurtosis36.19576555
Mean0.0239503055
Median Absolute Deviation (MAD)0.0175
Skewness4.127378462
Sum164.6344
Variance0.0009789419357
MonotocityNot monotonic
2021-04-20T13:39:51.612183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02036
27.0%
0.021722
 
0.3%
0.029421
 
0.3%
0.020820
 
0.3%
0.021319
 
0.3%
0.017219
 
0.3%
0.030319
 
0.3%
0.016719
 
0.3%
0.01918
 
0.2%
0.023818
 
0.2%
Other values (947)4663
61.9%
(Missing)661
 
8.8%
ValueCountFrequency (%)
02036
27.0%
0.00011
 
< 0.1%
0.00024
 
0.1%
0.00036
 
0.1%
0.00046
 
0.1%
ValueCountFrequency (%)
0.53331
< 0.1%
0.43691
< 0.1%
0.42651
< 0.1%
0.38971
< 0.1%
0.34591
< 0.1%

UGDS_NRA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct920
Distinct (%)13.4%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.01608581612
Minimum0
Maximum0.9286
Zeros3906
Zeros (%)51.8%
Memory size59.0 KiB
2021-04-20T13:39:51.720075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0117
95-th percentile0.078135
Maximum0.9286
Range0.9286
Interquartile range (IQR)0.0117

Descriptive statistics

Standard deviation0.05017188539
Coefficient of variation (CV)3.119013982
Kurtosis101.2317396
Mean0.01608581612
Median Absolute Deviation (MAD)0
Skewness8.284843909
Sum110.5739
Variance0.002517218083
MonotocityNot monotonic
2021-04-20T13:39:51.824886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03906
51.8%
0.001827
 
0.4%
0.001524
 
0.3%
0.000924
 
0.3%
0.000723
 
0.3%
0.00222
 
0.3%
0.002922
 
0.3%
0.00321
 
0.3%
0.001221
 
0.3%
0.001320
 
0.3%
Other values (910)2764
36.7%
(Missing)661
 
8.8%
ValueCountFrequency (%)
03906
51.8%
0.00017
 
0.1%
0.00028
 
0.1%
0.000311
 
0.1%
0.000413
 
0.2%
ValueCountFrequency (%)
0.92861
< 0.1%
0.89421
< 0.1%
0.87181
< 0.1%
0.81
< 0.1%
0.79441
< 0.1%

UGDS_UNKN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1517
Distinct (%)22.1%
Missing661
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.0451814373
Minimum0
Maximum0.9027
Zeros2067
Zeros (%)27.4%
Memory size59.0 KiB
2021-04-20T13:39:51.933279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0143
Q30.0454
95-th percentile0.200575
Maximum0.9027
Range0.9027
Interquartile range (IQR)0.0454

Descriptive statistics

Standard deviation0.0934404091
Coefficient of variation (CV)2.06811502
Kurtosis23.72572674
Mean0.0451814373
Median Absolute Deviation (MAD)0.0143
Skewness4.353223011
Sum310.5772
Variance0.008731110053
MonotocityNot monotonic
2021-04-20T13:39:52.041420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02067
27.4%
0.008521
 
0.3%
0.006820
 
0.3%
0.018219
 
0.3%
0.00218
 
0.2%
0.009817
 
0.2%
0.005616
 
0.2%
0.004515
 
0.2%
0.017515
 
0.2%
0.009415
 
0.2%
Other values (1507)4651
61.7%
(Missing)661
 
8.8%
ValueCountFrequency (%)
02067
27.4%
0.00011
 
< 0.1%
0.00024
 
0.1%
0.00034
 
0.1%
0.00046
 
0.1%
ValueCountFrequency (%)
0.90271
< 0.1%
0.87851
< 0.1%
0.87451
< 0.1%
0.86681
< 0.1%
0.86251
< 0.1%

PPTUG_EF
Real number (ℝ≥0)

MISSING
ZEROS

Distinct3420
Distinct (%)49.9%
Missing682
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean0.2266389902
Minimum0
Maximum1
Zeros1903
Zeros (%)25.3%
Memory size59.0 KiB
2021-04-20T13:39:52.156638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1504
Q30.3769
95-th percentile0.71062
Maximum1
Range1
Interquartile range (IQR)0.3769

Descriptive statistics

Standard deviation0.2464701974
Coefficient of variation (CV)1.087501304
Kurtosis0.216493156
Mean0.2266389902
Median Absolute Deviation (MAD)0.1504
Skewness1.019221488
Sum1553.157
Variance0.06074755822
MonotocityNot monotonic
2021-04-20T13:39:52.269084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01903
 
25.3%
144
 
0.6%
0.333315
 
0.2%
0.2512
 
0.2%
0.511
 
0.1%
0.210
 
0.1%
0.14298
 
0.1%
0.28578
 
0.1%
0.23087
 
0.1%
0.47
 
0.1%
Other values (3410)4828
64.1%
(Missing)682
 
9.1%
ValueCountFrequency (%)
01903
25.3%
0.00021
 
< 0.1%
0.00031
 
< 0.1%
0.00041
 
< 0.1%
0.00072
 
< 0.1%
ValueCountFrequency (%)
144
0.6%
0.99591
 
< 0.1%
0.9951
 
< 0.1%
0.99421
 
< 0.1%
0.99361
 
< 0.1%

CURROPER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 KiB
1
6957 
0
 
578

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7535
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%
2021-04-20T13:39:52.485082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T13:39:52.551342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%

Most occurring characters

ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7535
100.0%

Most frequent character per category

ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common7535
100.0%

Most frequent character per script

ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7535
100.0%

Most frequent character per block

ValueCountFrequency (%)
16957
92.3%
0578
 
7.7%

PCTPELL
Real number (ℝ≥0)

MISSING

Distinct4422
Distinct (%)64.6%
Missing686
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean0.5306430574
Minimum0
Maximum1
Zeros47
Zeros (%)0.6%
Memory size59.0 KiB
2021-04-20T13:39:52.980391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.3578
median0.5215
Q30.7129
95-th percentile0.89636
Maximum1
Range1
Interquartile range (IQR)0.3551

Descriptive statistics

Standard deviation0.2255443558
Coefficient of variation (CV)0.425039681
Kurtosis-0.7846975123
Mean0.5306430574
Median Absolute Deviation (MAD)0.1763
Skewness2.474414699 × 105
Sum3634.3743
Variance0.05087025644
MonotocityNot monotonic
2021-04-20T13:39:53.098693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166
 
0.9%
047
 
0.6%
0.530
 
0.4%
0.814
 
0.2%
0.7513
 
0.2%
0.412
 
0.2%
0.666711
 
0.1%
0.62511
 
0.1%
0.714310
 
0.1%
0.83339
 
0.1%
Other values (4412)6626
87.9%
(Missing)686
 
9.1%
ValueCountFrequency (%)
047
0.6%
0.00411
 
< 0.1%
0.01241
 
< 0.1%
0.01791
 
< 0.1%
0.01941
 
< 0.1%
ValueCountFrequency (%)
166
0.9%
0.99851
 
< 0.1%
0.99641
 
< 0.1%
0.99411
 
< 0.1%
0.99371
 
< 0.1%

PCTFLOAN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct4155
Distinct (%)60.7%
Missing686
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean0.5222108629
Minimum0
Maximum1
Zeros687
Zeros (%)9.1%
Memory size59.0 KiB
2021-04-20T13:39:53.234452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3329
median0.5833
Q30.745
95-th percentile0.89792
Maximum1
Range1
Interquartile range (IQR)0.4121

Descriptive statistics

Standard deviation0.2836155209
Coefficient of variation (CV)0.5431053642
Kurtosis-0.8120751355
Mean0.5222108629
Median Absolute Deviation (MAD)0.1902
Skewness-0.5229443778
Sum3576.6222
Variance0.08043776368
MonotocityNot monotonic
2021-04-20T13:39:53.362536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0687
 
9.1%
155
 
0.7%
0.517
 
0.2%
0.7515
 
0.2%
0.814
 
0.2%
0.833311
 
0.1%
0.610
 
0.1%
0.666710
 
0.1%
0.55568
 
0.1%
0.81827
 
0.1%
Other values (4145)6015
79.8%
(Missing)686
 
9.1%
ValueCountFrequency (%)
0687
9.1%
0.00061
 
< 0.1%
0.00111
 
< 0.1%
0.00181
 
< 0.1%
0.00241
 
< 0.1%
ValueCountFrequency (%)
155
0.7%
0.99761
 
< 0.1%
0.99531
 
< 0.1%
0.99441
 
< 0.1%
0.99411
 
< 0.1%

UG25ABV
Real number (ℝ≥0)

MISSING

Distinct4285
Distinct (%)63.8%
Missing817
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean0.4100211968
Minimum0
Maximum1
Zeros51
Zeros (%)0.7%
Memory size59.0 KiB
2021-04-20T13:39:53.481757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0374
Q10.2415
median0.40075
Q30.572275
95-th percentile0.8
Maximum1
Range1
Interquartile range (IQR)0.330775

Descriptive statistics

Standard deviation0.2289391657
Coefficient of variation (CV)0.5583593421
Kurtosis-0.672285885
Mean0.4100211968
Median Absolute Deviation (MAD)0.16515
Skewness0.1626538357
Sum2754.5224
Variance0.05241314159
MonotocityNot monotonic
2021-04-20T13:39:53.598669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051
 
0.7%
0.529
 
0.4%
0.333322
 
0.3%
0.666719
 
0.3%
0.415
 
0.2%
0.428615
 
0.2%
0.444413
 
0.2%
0.2512
 
0.2%
112
 
0.2%
0.555612
 
0.2%
Other values (4275)6518
86.5%
(Missing)817
 
10.8%
ValueCountFrequency (%)
051
0.7%
0.00051
 
< 0.1%
0.00061
 
< 0.1%
0.00071
 
< 0.1%
0.00081
 
< 0.1%
ValueCountFrequency (%)
112
0.2%
0.98961
 
< 0.1%
0.9881
 
< 0.1%
0.98441
 
< 0.1%
0.98281
 
< 0.1%

MD_EARN_WNE_P10
Categorical

HIGH CARDINALITY
MISSING

Distinct598
Distinct (%)9.3%
Missing1122
Missing (%)14.9%
Memory size59.0 KiB
PrivacySuppressed
822 
38800
 
151
21500
 
97
49200
 
78
27400
 
46
Other values (593)
5219 

Length

Max length17
Median length5
Mean length6.541712147
Min length4

Characters and Unicode

Total characters41952
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)2.1%

Sample

1st row30300
2nd row39700
3rd row40100
4th row45500
5th row26600
ValueCountFrequency (%)
PrivacySuppressed822
 
10.9%
38800151
 
2.0%
2150097
 
1.3%
4920078
 
1.0%
2740046
 
0.6%
3700045
 
0.6%
2880042
 
0.6%
2970042
 
0.6%
2530038
 
0.5%
2240034
 
0.5%
Other values (588)5018
66.6%
(Missing)1122
 
14.9%
2021-04-20T13:39:53.844415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
privacysuppressed822
 
12.8%
38800151
 
2.4%
2150097
 
1.5%
4920078
 
1.2%
2740046
 
0.7%
3700045
 
0.7%
2880042
 
0.7%
2970042
 
0.7%
2530038
 
0.6%
2940034
 
0.5%
Other values (588)5018
78.2%

Most occurring characters

ValueCountFrequency (%)
012228
29.1%
23012
 
7.2%
32673
 
6.4%
41934
 
4.6%
11868
 
4.5%
r1644
 
3.9%
p1644
 
3.9%
e1644
 
3.9%
s1644
 
3.9%
51442
 
3.4%
Other values (13)12219
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27978
66.7%
Lowercase Letter12330
29.4%
Uppercase Letter1644
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
r1644
13.3%
p1644
13.3%
e1644
13.3%
s1644
13.3%
i822
6.7%
v822
6.7%
a822
6.7%
c822
6.7%
y822
6.7%
u822
6.7%
ValueCountFrequency (%)
012228
43.7%
23012
 
10.8%
32673
 
9.6%
41934
 
6.9%
11868
 
6.7%
51442
 
5.2%
81331
 
4.8%
91171
 
4.2%
61165
 
4.2%
71154
 
4.1%
ValueCountFrequency (%)
P822
50.0%
S822
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common27978
66.7%
Latin13974
33.3%

Most frequent character per script

ValueCountFrequency (%)
r1644
11.8%
p1644
11.8%
e1644
11.8%
s1644
11.8%
P822
 
5.9%
i822
 
5.9%
v822
 
5.9%
a822
 
5.9%
c822
 
5.9%
y822
 
5.9%
Other values (3)2466
17.6%
ValueCountFrequency (%)
012228
43.7%
23012
 
10.8%
32673
 
9.6%
41934
 
6.9%
11868
 
6.7%
51442
 
5.2%
81331
 
4.8%
91171
 
4.2%
61165
 
4.2%
71154
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII41952
100.0%

Most frequent character per block

ValueCountFrequency (%)
012228
29.1%
23012
 
7.2%
32673
 
6.4%
41934
 
4.6%
11868
 
4.5%
r1644
 
3.9%
p1644
 
3.9%
e1644
 
3.9%
s1644
 
3.9%
51442
 
3.4%
Other values (13)12219
29.1%

GRAD_DEBT_MDN_SUPP
Categorical

HIGH CARDINALITY

Distinct2038
Distinct (%)27.2%
Missing32
Missing (%)0.4%
Memory size59.0 KiB
PrivacySuppressed
1510 
9500
514 
27000
 
306
25827.5
 
136
25000
 
124
Other values (2033)
4913 

Length

Max length17
Median length5
Mean length7.392376383
Min length4

Characters and Unicode

Total characters55465
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1542 ?
Unique (%)20.6%

Sample

1st row33888
2nd row21941.5
3rd row23370
4th row24097
5th row33118.5
ValueCountFrequency (%)
PrivacySuppressed1510
 
20.0%
9500514
 
6.8%
27000306
 
4.1%
25827.5136
 
1.8%
25000124
 
1.6%
12000118
 
1.6%
2000090
 
1.2%
983389
 
1.2%
1414488
 
1.2%
36173.581
 
1.1%
Other values (2028)4447
59.0%
2021-04-20T13:39:54.082882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
privacysuppressed1510
 
20.1%
9500514
 
6.9%
27000306
 
4.1%
25827.5136
 
1.8%
25000124
 
1.7%
12000118
 
1.6%
2000090
 
1.2%
983389
 
1.2%
1414488
 
1.2%
36173.581
 
1.1%
Other values (2028)4447
59.3%

Most occurring characters

ValueCountFrequency (%)
07539
 
13.6%
53827
 
6.9%
23615
 
6.5%
13368
 
6.1%
r3020
 
5.4%
p3020
 
5.4%
e3020
 
5.4%
s3020
 
5.4%
32113
 
3.8%
72011
 
3.6%
Other values (14)20912
37.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28946
52.2%
Lowercase Letter22650
40.8%
Uppercase Letter3020
 
5.4%
Other Punctuation849
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
r3020
13.3%
p3020
13.3%
e3020
13.3%
s3020
13.3%
i1510
6.7%
v1510
6.7%
a1510
6.7%
c1510
6.7%
y1510
6.7%
u1510
6.7%
ValueCountFrequency (%)
07539
26.0%
53827
13.2%
23615
12.5%
13368
11.6%
32113
 
7.3%
72011
 
6.9%
91922
 
6.6%
61636
 
5.7%
81474
 
5.1%
41441
 
5.0%
ValueCountFrequency (%)
P1510
50.0%
S1510
50.0%
ValueCountFrequency (%)
.849
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common29795
53.7%
Latin25670
46.3%

Most frequent character per script

ValueCountFrequency (%)
r3020
11.8%
p3020
11.8%
e3020
11.8%
s3020
11.8%
P1510
 
5.9%
i1510
 
5.9%
v1510
 
5.9%
a1510
 
5.9%
c1510
 
5.9%
y1510
 
5.9%
Other values (3)4530
17.6%
ValueCountFrequency (%)
07539
25.3%
53827
12.8%
23615
12.1%
13368
11.3%
32113
 
7.1%
72011
 
6.7%
91922
 
6.5%
61636
 
5.5%
81474
 
4.9%
41441
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII55465
100.0%

Most frequent character per block

ValueCountFrequency (%)
07539
 
13.6%
53827
 
6.9%
23615
 
6.5%
13368
 
6.1%
r3020
 
5.4%
p3020
 
5.4%
e3020
 
5.4%
s3020
 
5.4%
32113
 
3.8%
72011
 
3.6%
Other values (14)20912
37.7%

Interactions

2021-04-20T13:39:17.566333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:17.675753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:17.762100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:17.871308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:17.990476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.085619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.176757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.266249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.362127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.455780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.578806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.721166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:18.918948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.021673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.110890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.205471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.306982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.417076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.514204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.620564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.719051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.808221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.899428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:19.996145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.088027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.192428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.341564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.470007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.571075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.663726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:20.760845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-20T13:39:42.872898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:42.961985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.051156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.143418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.269325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.383039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.484547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-20T13:39:43.616759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-20T13:39:54.210882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-20T13:39:54.433330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-20T13:39:54.682684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-20T13:39:54.901281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-20T13:39:55.086549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-20T13:39:43.932359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-20T13:39:44.947386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-20T13:39:45.357483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-20T13:39:46.013981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

INSTNMCITYSTABBRHBCUMENONLYWOMENONLYRELAFFILSATVRMIDSATMTMIDDISTANCEONLYUGDSUGDS_WHITEUGDS_BLACKUGDS_HISPUGDS_ASIANUGDS_AIANUGDS_NHPIUGDS_2MORUGDS_NRAUGDS_UNKNPPTUG_EFCURROPERPCTPELLPCTFLOANUG25ABVMD_EARN_WNE_P10GRAD_DEBT_MDN_SUPP
0Alabama A & M UniversityNormalAL1.00.00.00424.0420.00.04206.00.03330.93530.00550.00190.00240.00190.00000.00590.01380.065610.73560.82840.10493030033888
1University of Alabama at BirminghamBirminghamAL0.00.00.00570.0565.00.011383.00.59220.26000.02830.05180.00220.00070.03680.01790.01000.260710.34600.52140.24223970021941.5
2Amridge UniversityMontgomeryAL0.00.00.01NaNNaN1.0291.00.29900.41920.00690.00340.00000.00000.00000.00000.27150.453610.68010.77950.85404010023370
3University of Alabama in HuntsvilleHuntsvilleAL0.00.00.00595.0590.00.05451.00.69880.12550.03820.03760.01430.00020.01720.03320.03500.214610.30720.45960.26404550024097
4Alabama State UniversityMontgomeryAL1.00.00.00425.0430.00.04811.00.01580.92080.01210.00190.00100.00060.00980.02430.01370.089210.73470.75540.12702660033118.5
5The University of AlabamaTuscaloosaAL0.00.00.00555.0565.00.029851.00.78250.11190.03480.01060.00380.00090.02610.02680.00260.084410.20400.40100.08534190023750
6Central Alabama Community CollegeAlexander CityAL0.00.00.00NaNNaN0.01592.00.72550.26130.00440.00250.00440.00000.00000.00000.00190.388210.58920.39770.31532750016127
7Athens State UniversityAthensAL0.00.00.00NaNNaN0.02991.00.78230.12000.01910.00530.01570.00100.01740.00570.03340.551710.40880.62960.64103900018595
8Auburn University at MontgomeryMontgomeryAL0.00.00.00486.0509.00.04304.00.53280.33760.00740.02210.00440.00160.02970.03970.02460.285310.41920.58030.29303500021335
9Auburn UniversityAuburnAL0.00.00.00575.0588.00.020514.00.85070.07040.02480.02270.00740.00000.00000.01000.01400.086210.16100.34940.04154570021831

Last rows

INSTNMCITYSTABBRHBCUMENONLYWOMENONLYRELAFFILSATVRMIDSATMTMIDDISTANCEONLYUGDSUGDS_WHITEUGDS_BLACKUGDS_HISPUGDS_ASIANUGDS_AIANUGDS_NHPIUGDS_2MORUGDS_NRAUGDS_UNKNPPTUG_EFCURROPERPCTPELLPCTFLOANUG25ABVMD_EARN_WNE_P10GRAD_DEBT_MDN_SUPP
7525Strayer University-North DallasDallasTXNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN36173.5
7526Strayer University-San AntonioSan AntonioTXNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN36173.5
7527Strayer University-StaffordStaffordTXNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN36173.5
7528WestMed College - MercedMercedCANaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN15623.5
7529Vantage CollegeEl PasoTXNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN9500
7530SAE Institute of Technology San FranciscoEmeryvilleCANaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN9500
7531Rasmussen College - Overland ParkOverland ParkKSNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN21163
7532National Personal Training Institute of ClevelandHighland HeightsOHNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN6333
7533Bay Area Medical Academy - San Jose Satellite LocationSan JoseCANaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaNPrivacySuppressed
7534Excel Learning Center-San Antonio SouthSan AntonioTXNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaNNaNNaNNaN12125